# A Probabilistic Programming Idiom for Active Knowledge Search

arXiv: https://arxiv.org/abs/2202.09555 · HTML: https://mlsanity.com/paper/2202.09555/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 19 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review](https://mlsanity.com/paper/1805.00909/) (2018)
- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [HouseExpo: A Large-scale 2D Indoor Layout Dataset for Learning-based Algorithms on Mobile Robots](https://mlsanity.com/paper/1903.09845/) (2019)
- [Frontier Based Exploration for Autonomous Robot](https://mlsanity.com/paper/1806.03581/) (2018)
- [Toward an Idiomatic Framework for Cognitive Robotics](https://mlsanity.com/paper/2111.13027/) (2021)

## Abstract (arXiv)

In this paper, we derive and implement a probabilistic programming idiom for the problem of acquiring new knowledge about an environment. The idiom is implemented utilizing a modern probabilistic programming language. We demonstrate the utility of this idiom by implementing an algorithm for the specific problem of active mapping and robot exploration. Finally, we evaluate the functionality of the implementation through an extensive simulation study utilizing the HouseExpo dataset.